A case study on designing business processes based on collaborative and mining approaches

Gonçalves, Juliana; Santoro, Flávia Maria; Baião, Fernanda

doi:10.1109/cscwd.2010.5471899

Cited by 11 publications

(14 citation statements)

References 13 publications

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“…Table 5 also suggests that there are few articles on the mining of professional experience in general: there are many more articles that investigate the reporting of non‐professional (personal) experience compared to professional experience. The two articles by Gonçalves et al [35, 36 ] are the only articles in our data set to examine professional experience. They use a tool to generate stories from the professional participants in contrast to, for example, mining reports of professional experience from practitioners’ grey literature.…”

Section: Classification Of the Articlesmentioning

confidence: 99%

Retrieving and mining professional experience of software practice from grey literature: an exploratory review

et al. 2020

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Retrieving and mining practitioners’ self‐reports of their professional experience of software practice could provide valuable evidence for research. The authors are, however, unaware of any existing reviews of research conducted in this area. The authors reviewed and classified previous research, and identified insights into the challenges research confronts when retrieving and mining practitioners’ self‐reports of their experience of software practice. They conducted an exploratory review to identify and classify 42 studies. They analysed a selection of those studies for insights on challenges to mining professional experience. They identified only one directly relevant study. Even then this study concerns the software professional's emotional experiences rather than the professional's reporting of behaviour and events occurring during software practice. They discussed the challenges concerning: the prevalence of professional experience; definitions, models and theories; the sparseness of data; units of discourse analysis; annotator agreement; evaluation of the performance of algorithms; and the lack of replications. No directly relevant prior research appears to have been conducted in this area. They discussed the value of reporting negative results in secondary studies. There are a range of research opportunities but also considerable challenges. They formulated a set of guiding questions for further research in this area.

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Section: Classification Of the Articlesmentioning

confidence: 99%

Retrieving and mining professional experience of software practice from grey literature: an exploratory review

et al. 2020

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“…by constricting the format of the textual input [17], but no study considers all mentioned problems and offers a comprehensive solution strategy. Another interesting fact is that none of the works using a shallow parser shows how they deal with passive voice [15,22,23,17]. We solved this problem by using the grammatical relations of the Stanford Parser.…”

Section: Transformation Approachmentioning

confidence: 99%

“…This approach was also taken by most of the other works which built a similar system [17,22,18,23]. The data structure used by the approach of the University of Rio de Janeiro [23] was taken from the CREWS project [15].…”

Section: Transformation Approachmentioning

confidence: 99%

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Process Model Generation from Natural Language Text

Friedrich¹,

Mendling

Puhlmann³

2011

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

170

191

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Abstract. Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60% of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use format. In this paper, we tackle this problem based on an automatic approach to generate BPMN models from natural language text. We combine existing tools from natural language processing in an innovative way and augmented them with a suitable anaphora resolution mechanism. The evaluation of our technique shows that for a set of 47 text-model pairs from industry and textbooks, we are able to generate on average 77% of the models correctly.

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“…Recent research has also started using natural language processing techniques for generating process models from text. Gonçalves et al generate process models from group stories [21]. The approach by the University of Klagenfurt combines linguistic analysis with user feedback [22].…”

Section: Related Workmentioning

confidence: 99%

On the Automatic Labeling of Process Models

Leopold

Mendling

Reijers

2011

Advanced Information Systems Engineering

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Abstract. Process models are essential tools for managing, understanding and changing business processes. Yet, from a user perspective they can quickly become too complex to deal with. Abstraction -aggregating detailed fragments into more coarse-grained ones -has proven to be a valuable technique to simplify the view on a process model. Various techniques that automate the decision of which model fragments to aggregate have been defined and validated by recent research, but their application is hampered by the lack of abilities to generate meaningful names for such aggregated parts. In this paper, we address this problem by investigating naming strategies for individual model fragments and process models as a whole. Our contribution is an automatic naming approach that builds on the linguistic analysis of process models from industry.

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A case study on designing business processes based on collaborative and mining approaches

Cited by 11 publications

References 13 publications

Retrieving and mining professional experience of software practice from grey literature: an exploratory review

Retrieving and mining professional experience of software practice from grey literature: an exploratory review

Process Model Generation from Natural Language Text

On the Automatic Labeling of Process Models

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